RAMBOAU: Risk-averse Multiobjective Bayesian Optimization with Aleatoric Uncertainty for Nanomaterial Synthesis
Abstract
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under different synthesis conditions. This can be misaligned with the need for reliable synthesis protocols that consistently produce materials with desired properties. This work introduces a risk-averse multi-objective Bayesian optimization algorithm that accounts for heteroscedastic experimental uncertainty in the outcomes of different synthesis conditions using a value-at-risk approach. It employs two Gaussian processes per objective, one for the expected function value and one for its variance, and optimizes a novel risk-averse acquisition function. Benchmarking against a state-of-the-art risk-neutral algorithm shows superior performance in identifying robust Pareto fronts. Applied to optimize the production of high-purity, high-aspect-ratio ZnO nanoparticles in a continuously operated annular flow microreactor in the laboratory, the algorithm identified reliable process conditions despite significant uncertainty. This work represents an important step towards autonomous platforms that can identify synthesis conditions that are robust enough for industrial scale-up and practical implementation.
Authors 5
-
Karim Ben Hicham Aachen
University of Cambridge · The Cambridge Centre for Advanced Research and Education in Singapore · RWTH Aachen University
Affiliation as printed
Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd
Department of Chemical Engineering and Biotechnology, University of Cambridge
RWTH Aachen University, Process Systems Engineering (AVT.SVT)
-
University of Cambridge · The Cambridge Centre for Advanced Research and Education in Singapore
Affiliation as printed
Accelerated Materials Ltd
Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd
Department of Chemical Engineering and Biotechnology, University of Cambridge
-
University of Cambridge · The Cambridge Centre for Advanced Research and Education in Singapore
Affiliation as printed
Accelerated Materials Ltd
Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd
Department of Chemical Engineering and Biotechnology, University of Cambridge
-
Jan G. Rittig Aachen
École Polytechnique Fédérale de Lausanne · RWTH Aachen University
Affiliation as printed
EPFL, Laboratory of Artificial Chemical Intelligence (LIAC)
RWTH Aachen University, Process Systems Engineering (AVT.SVT)
-
University of Cambridge · The Cambridge Centre for Advanced Research and Education in Singapore
Affiliation as printed
Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd
Department of Chemical Engineering and Biotechnology, University of Cambridge
Cited by 0 stored of 0
No patents citing this paper on Lens.org (checked 2026-10-06).